Monitoring compliance with environmental regulations is a global challenge. It is particularly difficult for governments in low-income countries, where informal industry is responsible for a large amount of pollution, because the governments lack the ability to locate and monitor large numbers of dispersed polluters. This study demonstrates an accurate, scalable machine learning approach for identifying brick kilns, a highly polluting informal industry in Bangladesh, in satellite imagery.Our data reveal widespread violations of the national regulations governing brick manufacturing, which has implications for the health and well-being of the country. Our approach offers a low-cost, replicable method for regulatory agencies to generate information on key pollution sources.